Biomedical Knowledge Derivation from Scientific Publications via Dual-Graph Resonance

Anonymous

Abstract: Scientific Information Extraction (SciIE) is an important task and increasingly being applied in biomedical searching to conceptualize and epitomize knowledge triplets from scientific literature. Existing relation extraction methods aim to extract explicit triplet knowledge from documents, however they can hardly perceive unobserved factual relations. Recent generative methods have more flexibility, but their generated relations will encounter trustworthiness problems. In this paper, we first propose a novel Extraction-Contextualization-Derivation (ECD) strategy to generate document-specific and entity-expanded dynamic graph from a shared static knowledge graph. Then, we introduce an extensible Dual-Graph Resonance Network (DGRN) which can generate richer explicit and implicit relations under the guidance of static and dynamic knowledge graphs. Experiments conducted on a public PubMed corpus validate the superiority of our method against several state-of-the-art baselines.

Paper Type: long

Research Area: Information Extraction